Photo by Artturi Jalli on Unsplash
It started the night I had three scripts running side-by-side, each trying to solve a problem I didn’t want to touch manually. One script scraped a client’s inventory, another cleaned the data, and the last one generated a PDF report while I made coffee. I realized then, coding like the top 1% isn’t about knowing “more” Python. It’s about knowing the right tools that turn hours of work into seconds of automation.
And here’s the truth: most Python developers never touch these libraries, which is why they stay in the 99%. You’re about to change that.
1. Pendulum-lite Alternative
Arrow is the fast, intuitive datetime library you didn’t know you needed. It removes the boilerplate that comes with Python’s datetime.
import arrowprint(arrow.now().shift(days=+3).format('YYYY-MM-DD'))
Perfect for automation scripts that schedule tasks, generate logs, or handle timezone conversions.
2. Faker
Why waste time typing fake names and emails? Faker generates them in milliseconds.
from faker import Fakerfake = Faker()print(fake.name(), fake.email())
It’s a lifesaver when automating tests or mocking datasets.
3. TQDM
Automations often run in the background, but sometimes you need visibility. TQDM wraps any iterable with a progress bar.
from tqdm import tqdmimport timefor _ in tqdm(range(100)): time.sleep(0.01)
When running big automation jobs, this keeps you from wondering, “Is this still running?”
4. YAML
YAML lets you store automation settings in a cleaner, human-readable way.
import yamlconfig = yaml.safe_load(open('config.yml'))print(config['api_key'])
Instead of hardcoding credentials, you keep them in a .yml file and load them dynamically.
5. Boto3
From uploading files to S3 to spinning up EC2 instances, Boto3 makes AWS scripting painless.
import boto3s3 = boto3.client('s3')s3.upload_file('report.pdf', 'my-bucket', 'report.pdf')
You can build full cloud automation workflows without touching the AWS console.
6. Airflow
Airflow isn’t just for big companies. It schedules and manages complex automation pipelines.
pip install apache-airflow
Imagine chaining scripts so they run in perfect order. Airflow does exactly that.
7. PyPDF2
Whether it’s merging multiple PDFs or extracting text, PyPDF2 lets you automate the process in seconds.
from PyPDF2 import PdfMergermerger = PdfMerger()merger.append('file1.pdf')merger.append('file2.pdf')merger.write('combined.pdf')merger.close()
No more dragging files into random online PDF tools.
8. FastF1
For automation in niche fields, FastF1 is a Formula 1 data API wrapper. It’s perfect for sports dashboards and analytics.
import fastf1fastf1.Cache.enable_cache('cache')session = fastf1.get_session(2023, 'Monaco', 'Q')session.load()print(session.results)
Shows how automation isn’t limited to “business” work, it can be for fun too.
9. Watchfiles
Think watchdog, but faster. Watchfiles runs functions automatically when files change.
from watchfiles import watchfor changes in watch('./data'): print(changes)
Great for instant triggers when a file is updated, downloaded, or replaced.
Closing Words:
Don’t just “collect” libraries, master them. The difference between a good Python developer and a top 1% developer is not knowing what exists, but knowing exactly when to use it.
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